The classical parametric estimation approach, maximum likelihood, while providing maximally efficient estimators at the correct model, lacks robustness. As a modification of maximum likelihood, Huber (1964) introduced M-estimators, which are very general but often ad hoc. Basu et al. (1998) developed a family of density-based divergences, many of which exhibit robustness. It turns out that maximum likelihood is a special case of this general class of divergence functions, which are indexed by a parameter alpha. Basu noted that only values of alpha in the [0,1] range were of interest -- with alpha = 0 giving the maximum likelihood solution and alpha = 1 the L2E solution (Scott, 2001). As alpha increases, there is a clear tradeoff between increasing robustness and decreasing efficiency. This thesis develops a family of robust location and scale estimators by applying Basu's alpha-divergence function to a multivariate partial density component model (Scott, 2004). The usefulness of alpha values greater than 1 will be explored, and the new estimator will be applied to simulated cases and applications in parametric density estimation and regression.

Many minimum distance estimators have the potential to provide parameter estimates which are both robust and efficient and yet, despite these highly desirable theoretical properties, they are rarely used in practice. This is because the performance of these estimators is rarely guaranteed per se but obtained by placing a suitable value on some tuning parameter. Hence there is a risk involved in implementing these methods because if the value chosen for the tuning parameter is inappropriate for the data to which the method is applied, the resulting estimators may not have the desired theoretical properties and could even perform less well than one of the simpler, more widely used alternatives. There are currently no data-based methods available for deciding what value one should place on these tuning parameters hence the primary aim of this research is to develop an objective way of selecting values for the tuning parameters in minimum distance estimators so that the full potential of these estimators might be realised. This new method was initially developed to optimise the performance of the density power divergence estimator, which was proposed by Basu, Harris, Hjort and Jones [3]. The results were very promising so the method was then applied to two other minimum distance estimators and the results compared.

La régulation de la concentration de glucose dans l'organisme est nécessaire au bon fonctionnement des globules rouges et de l'ensemble des cellules, dont celles des muscles et du cerveau. Cette régulation met en jeu plusieurs organes ainsi que le système hormonal dont une hormone en particulier, l’insuline. Le diabète de type 1 est une maladie où les cellules productrices d'insuline du pancréas sont détruites. Afin de compenser cette perte de production d'insuline, le traitement de cette maladie consiste, pour le patient, à déterminer une dose d'insuline à s'injecter en fonction de mesures de sa glycémie et de certaines caractéristiques intervenant dans la régulation de celle-ci (repas, activité physique, stress,...). Cette thèse s'inscrit dans une démarche d’automatisation du traitement en proposant un nouveau modèle non-linéaire du métabolisme glucidique pouvant être utilisé dans une solution de contrôle en boucle fermée. Nous avons prouvé que ce modèle possède une unique solution positive et bornée pour des conditions initiales fixées et sa commandabilité locale. Nous nous sommes ensuite intéressés à l’identification paramétrique de ce modèle. Nous avons montré son identifiabilité structurelle et pratique. Dans ce cadre, une nouvelle méthodologie permettant de qualifier l'identifiabilité pratique d'un modèle, basée sur une divergence de Kullback-Leibler, a été proposée. Une estimation des paramètres du modèle a été réalisée à partir de données de patients réels. Dans ce but, une méthodologie d'estimation robuste, basée sur un critère de Huber, a été utilisée. Les résultats obtenus ont montré la pertinence du nouveau modèle proposé. / The development of new control models to represent more accurately the plasma glucose-insulin dynamics in T1DM is needed for efficient closed-loop algorithms. In this PhD thesis, we proposed a new nonlinear model of five time-continuous state equations with the aim to identify its parameters from easily available real patients' data (i.e. data from the insulin pump and the glucose monitoring system. Its design is based on two assumptions. Firstly, two successive remote compartments, one for insulin and one for glucose issued from the meal, are introduced to account for the distribution of the insulin and the glucose in the organism. Secondly, the insulin action in glucose disappearance is modeled through an original nonlinear form. The mathematical properties of this model have been studied and we proved that a unique, positive and bounded solution exists for a fixed initial condition. It is also shown that the model is locally accessible. In this way, it can so be used as a control model. We proved the structural identifiability of this model and proposed a new method based on the Kullback-Leiber divergence in view to test its practical identifiability. The parameters of the model were estimated from real patients' data. The obtained mean fit indicates a good approximation of the glucose metabolism of real patients. The predictions of the model approximate accurately the glycemia of the studied patients during few hours. Finally, the obtained results let us validate the relevance of this new model as a control model in view to be applied to closed-loop algorithms.